Yvonne Kao

Overview

Yvonne Kao (she/her) is a computer science, data science, and mathematics education researcher who focuses on the design and evaluation of instructional materials and educational technology for all age groups. She is most energized by working directly with teachers, teacher leaders, and product developers through research-practice partnerships that address significant problems of educational practice. Her research uses a range of approaches from large-scale cluster-randomized trials to smaller behavioral studies.

Recently, Kao led Computer Science Connections, a research–practice partnership between WestEd and Oakland Unified School District (OUSD) funded by the National Science Foundation. The goal of this project was to adapt and create data science lessons for OUSD’s middle school computer science classes. Code.org, a nonprofit dedicated to expanding access and broadening participation in computer science in K–12 schools, is currently adapting the lessons and will deploy them at scale as part of their data science curriculum.

Kao also regularly participates in statewide and national committees to improve computer science, data science, and mathematics education and the use of educational technology. Most recently, she was a member of the National Academies working group on data science assessment. In 2020 she served as the chair of the Vision for Teaching and Learning working group for the EdTech Genome Project sponsored by the EdTech Evidence Exchange. From 2017 to 2019, she was a writer for the California state computer science standards and strategic implementation plan.

Education

• PhD in cognitive psychology, Carnegie Mellon University
• BS in computer science, mathematics, and psychology, University of Wisconsin—Madison

Select Publications

Kao, Y., McKinney, D., Berg, S., Tuohy, B., & Ortega, C. (2024). Discourse practices in computer science education. In SIGCSE 2024: Proceedings of the 55th Technical Symposium on Computer Science Education (pp. 632–638). Association for Computing Machinery.

Kao, Y., Murphy, D. L., Hubbard Cheuoua, A., Kannan, P., Tsan, J., Jennings, K. E., Smith, H., Emanuel, S., & Miller, E. R. (2023). The development and validation of a survey to predict computing career intentions. In K. Fisler, P. Denny, K. Franklin, & M. Hamilton (Eds.), ICER ’23: Proceedings of the 2023 ACM Conference on International Computing Education Research (Vo. 1, pp. 256–269). Association for Computing Machinery.

Kao, Y. S., Matlen, B., & Weintrop, D. (2022). From one language to the next: Applications of analogical transfer for programming education. ACM Transactions on Computing Education, 22(4), 1–21.

Kao, Y. S., Matlen, B., Tiu, M., & Li, L. (2017). Logic models as a framework for iterative user research in educational technology: Illustrative cases. In R. D. Roscoe, S. D. Craig, & I. Douglas (Eds.), End-user considerations in educational technology design (pp. 52–75). IGI Global.